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  • MDPI AG  (2)
  • Zhang, Huifang  (2)
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  • MDPI AG  (2)
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  • 1
    In: Molecules, MDPI AG, Vol. 25, No. 6 ( 2020-03-11), p. 1264-
    Abstract: Phenolamines and flavonoids are two important components in bee pollen. There are many reports on the bioactivity of flavonoids in bee pollen, but few on phenolamines. This study aims to separate and characterize the flavonoids and phenolamines from rape bee pollen, and compare their antioxidant activities and protective effects against oxidative stress. The rape bee pollen was separated to obtain 35% and 50% fractions, which were characterized by HPLC-ESI-QTOF-MS/MS. The results showed that the compounds in 35% fraction were quercetin and kaempferol glycosides, while the compounds in 50% fraction were phenolamines, including di-p-coumaroyl spermidine, p-coumaroyl caffeoyl hydroxyferuloyl spermine, di-p-coumaroyl hydroxyferuloyl spermine, and tri-p-coumaroyl spermidine. The antioxidant activities of phenolamines and flavonoids were evaluated by 2,2-diphenyl-1-picrylhydrazyl (DPPH), 2,2’-azino-bis-3-ethylbenzothiazoline-6-sulphonic acid (ABTS), and ferric reducing antioxidant power (FRAP) assays. It was found that the antioxidant activity of phenolamines was significantly higher than that of flavonoids. Moreover, phenolamines showed better protective effects than flavonoids on HepG2 cells injured by AAPH. Furthermore, phenolamines could significantly reduce the reactive oxygen species (ROS), alanine aminotransferase (ALT) and aspartate aminotransferase (AST) levels, and increase the superoxide dismutase (SOD) and glutathione (GSH) levels. This study lays a foundation for the further understanding of phenolamines in rape bee pollen.
    Type of Medium: Online Resource
    ISSN: 1420-3049
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2008644-1
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  • 2
    In: Atmosphere, MDPI AG, Vol. 9, No. 11 ( 2018-11-05), p. 428-
    Abstract: In this study, we evaluated estimates and predictions of the PM2.5 (fine particulate matter) concentrations and emissions in Xuzhou, China, using a coupled Lagrangian particle dispersion modeling system (FLEXPART-WRF). A Bayesian inversion method was used in FLEXPART-WRF to improve the emission calculation and mixing ratio estimation for PM2.5. We first examined the inversion modeling performance by comparing the model predictions with PM2.5 concentration observations from four stations in Xuzhou. The linear correlation analysis between the predicted PM2.5 concentrations and the observations shows that our inversion forecast system is much better than the system before calibration (with correlation coefficients of R = 0.639 vs. 0.459, respectively, and root mean square errors of RMSE = 7.407 vs. 9.805 µg/m3, respectively). We also estimated the monthly average emission flux in Xuzhou to be 4188.26 Mg/month, which is much higher (by ~10.12%) than the emission flux predicted by the multiscale emission inventory data (MEIC) (3803.5 Mg/month). In addition, the monthly average emission flux shows obvious seasonal variation, with the lowest PM2.5 flux in summer and the highest flux in winter. This pattern is mainly due to the additional heating fuels used in the cold season, resulting in many fine particulates in the atmosphere. Although the inversion and forecast results were improved to some extent, the inversion system can be improved further, e.g., by increasing the number of observation values and improving the accuracy of the a priori emission values. Further research and analysis are recommended to help improve the forecast precision of real-time PM2.5 concentrations and the corresponding monthly emission fluxes.
    Type of Medium: Online Resource
    ISSN: 2073-4433
    Language: English
    Publisher: MDPI AG
    Publication Date: 2018
    detail.hit.zdb_id: 2605928-9
    SSG: 23
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